Abstract
As the spread of digital videos, digital cameras, and camera phones, lots of researches are reported about degraded character recognition. It is found that while the grayscale-based classifier is powerful for degraded character, the performance for clear character is not so good as binary-based classifier. In this paper, a dynamic classifier selection method is proposed to combine the two classifiers based on an estimation of the degradation level and the recognition reliability of the input character images. Experimental results show that the proposed method can achieve better recognition performance than the two individual ones.
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© 2006 Springer-Verlag Berlin Heidelberg
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Hotta, Y., Sun, J., Katsuyama, Y., Naoi, S. (2006). Robust Chinese Character Recognition by Selection of Binary-Based and Grayscale-Based Classifier. In: Bunke, H., Spitz, A.L. (eds) Document Analysis Systems VII. DAS 2006. Lecture Notes in Computer Science, vol 3872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11669487_49
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DOI: https://doi.org/10.1007/11669487_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32140-8
Online ISBN: 978-3-540-32157-6
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